Package `patchwork` required for this function to work.package 'patchwork' successfully unpacked and MD5 sums checked
The downloaded binary packages are in
C:\Users\kevin\AppData\Local\Temp\RtmpULxrCp\downloaded_packages
| Metric | Value |
|---|---|
| AIC | 16802.99 |
| AICc | 16803.13 |
| BIC | 16919.57 |
| R2 (cond.) | 0.03 |
| R2 (marg.) | 0.02 |
| ICC | 5.55e-03 |
| RMSE | 1.37 |
| Sigma | 1.38 |
For interpretation of performance metrics, please refer to this documentation.
| Parameter | Coefficient | SE | 95% CI | t(4783) | p |
|---|---|---|---|---|---|
| (Intercept) | 5.95 | 0.38 | (5.20, 6.69) | 15.71 | < .001 |
| educacion rec [Ed Media] | -0.16 | 0.08 | (-0.32, -4.19e-03) | -2.01 | 0.044 |
| educacion rec [Ed Tecnica] | -0.14 | 0.09 | (-0.31, 0.03) | -1.64 | 0.101 |
| educacion rec [Universidad o posgrado] | -0.08 | 0.09 | (-0.25, 0.09) | -0.92 | 0.358 |
| educacion rec [Ns/Nr] | -0.02 | 0.08 | (-0.17, 0.14) | -0.20 | 0.842 |
| libros rec [Más de 25] | 0.04 | 0.04 | (-0.05, 0.12) | 0.81 | 0.419 |
| apod acc div | 0.07 | 0.02 | (0.04, 0.11) | 3.93 | < .001 |
| civic [Nivel D] | 0.19 | 0.12 | (-0.04, 0.41) | 1.63 | 0.103 |
| civic [Nivel C] | 0.36 | 0.11 | (0.14, 0.58) | 3.27 | 0.001 |
| civic [Nivel B] | 0.54 | 0.11 | (0.32, 0.76) | 4.89 | < .001 |
| civic [Nivel A] | 0.62 | 0.11 | (0.40, 0.85) | 5.48 | < .001 |
| apdis fa | 0.07 | 0.02 | (0.02, 0.12) | 2.69 | 0.007 |
| mean apdis fa | -0.05 | 0.08 | (-0.22, 0.11) | -0.63 | 0.527 |
| etnia2 | 0.08 | 0.08 | (-0.08, 0.23) | 0.96 | 0.335 |
| inmigrante2 | 0.29 | 0.58 | (-0.85, 1.43) | 0.50 | 0.619 |
| escolaridad | -0.04 | 0.04 | (-0.12, 0.04) | -0.90 | 0.368 |
| Parameter | Coefficient |
|---|---|
| SD (Intercept: mrbd) | 0.10 |
| SD (Residual) | 1.38 |
To find out more about table summary options, please refer to this documentation.
| educacion_rec | mrbd | Predicted | SE | 95% CI |
|---|---|---|---|---|
| Ed Basica | 0.00 | 6.15 | ||
| Ed Media | 0.00 | 5.99 | ||
| Ed Tecnica | 0.00 | 6.01 | ||
| Universidad o posgrado | 0.00 | 6.07 | ||
| Ns/Nr | 0.00 | 6.14 |
Variable predicted: est_acc_div
Predictors modulated: educacion_rec
Predictors controlled: libros_rec (1), apod_acc_div (6.6), civic (1), apdis_fa (-0.0072), mean_apdis_fa (-0.0036), etnia2 (0.35), inmigrante2 (0.019), escolaridad (8.6)
| libros_rec | mrbd | Predicted | SE | 95% CI |
|---|---|---|---|---|
| Menos de 25 | 0.00 | 6.15 | ||
| Más de 25 | 0.00 | 6.19 |
Variable predicted: est_acc_div
Predictors modulated: libros_rec
Predictors controlled: educacion_rec (1), apod_acc_div (6.6), civic (1), apdis_fa (-0.0072), mean_apdis_fa (-0.0036), etnia2 (0.35), inmigrante2 (0.019), escolaridad (8.6)
| apod_acc_div | mrbd | Predicted | SE | 95% CI |
|---|---|---|---|---|
| 2.39 | 0.00 | 5.84 | ||
| 3.45 | 0.00 | 5.92 | ||
| 4.50 | 0.00 | 5.99 | ||
| 5.55 | 0.00 | 6.07 | ||
| 6.60 | 0.00 | 6.15 | ||
| 7.65 | 0.00 | 6.23 | ||
| 8.70 | 0.00 | 6.31 | ||
| 9.75 | 0.00 | 6.39 | ||
| 10.81 | 0.00 | 6.47 | ||
| 11.86 | 0.00 | 6.54 |
Variable predicted: est_acc_div
Predictors modulated: apod_acc_div
Predictors controlled: educacion_rec (1), libros_rec (1), civic (1), apdis_fa (-0.0072), mean_apdis_fa (-0.0036), etnia2 (0.35), inmigrante2 (0.019), escolaridad (8.6)
| civic | mrbd | Predicted | SE | 95% CI |
|---|---|---|---|---|
| Bajo nivel D | 0.00 | 6.15 | ||
| Nivel D | 0.00 | 6.34 | ||
| Nivel C | 0.00 | 6.51 | ||
| Nivel B | 0.00 | 6.69 | ||
| Nivel A | 0.00 | 6.77 |
Variable predicted: est_acc_div
Predictors modulated: civic
Predictors controlled: educacion_rec (1), libros_rec (1), apod_acc_div (6.6), apdis_fa (-0.0072), mean_apdis_fa (-0.0036), etnia2 (0.35), inmigrante2 (0.019), escolaridad (8.6)
| apdis_fa | mrbd | Predicted | SE | 95% CI |
|---|---|---|---|---|
| -3.40 | 0.00 | 5.92 | ||
| -2.56 | 0.00 | 5.98 | ||
| -1.71 | 0.00 | 6.04 | ||
| -0.86 | 0.00 | 6.09 | ||
| -7.24e-03 | 0.00 | 6.15 | ||
| 0.84 | 0.00 | 6.21 | ||
| 1.69 | 0.00 | 6.26 | ||
| 2.54 | 0.00 | 6.32 | ||
| 3.39 | 0.00 | 6.38 | ||
| 4.24 | 0.00 | 6.43 |
Variable predicted: est_acc_div
Predictors modulated: apdis_fa
Predictors controlled: educacion_rec (1), libros_rec (1), apod_acc_div (6.6), civic (1), mean_apdis_fa (-0.0036), etnia2 (0.35), inmigrante2 (0.019), escolaridad (8.6)
| mean_apdis_fa | mrbd | Predicted | SE | 95% CI |
|---|---|---|---|---|
| -1.09 | 0.00 | 6.21 | ||
| -0.82 | 0.00 | 6.19 | ||
| -0.55 | 0.00 | 6.18 | ||
| -0.28 | 0.00 | 6.17 | ||
| -3.65e-03 | 0.00 | 6.15 | ||
| 0.27 | 0.00 | 6.14 | ||
| 0.54 | 0.00 | 6.12 | ||
| 0.81 | 0.00 | 6.11 | ||
| 1.08 | 0.00 | 6.09 | ||
| 1.36 | 0.00 | 6.08 |
Variable predicted: est_acc_div
Predictors modulated: mean_apdis_fa
Predictors controlled: educacion_rec (1), libros_rec (1), apod_acc_div (6.6), civic (1), apdis_fa (-0.0072), etnia2 (0.35), inmigrante2 (0.019), escolaridad (8.6)
| etnia2 | mrbd | Predicted | SE | 95% CI |
|---|---|---|---|---|
| -0.90 | 0.00 | 6.06 | ||
| -0.59 | 0.00 | 6.08 | ||
| -0.28 | 0.00 | 6.10 | ||
| 0.04 | 0.00 | 6.13 | ||
| 0.35 | 0.00 | 6.15 | ||
| 0.66 | 0.00 | 6.18 | ||
| 0.97 | 0.00 | 6.20 | ||
| 1.29 | 0.00 | 6.22 | ||
| 1.60 | 0.00 | 6.25 | ||
| 1.91 | 0.00 | 6.27 |
Variable predicted: est_acc_div
Predictors modulated: etnia2
Predictors controlled: educacion_rec (1), libros_rec (1), apod_acc_div (6.6), civic (1), apdis_fa (-0.0072), mean_apdis_fa (-0.0036), inmigrante2 (0.019), escolaridad (8.6)
| inmigrante2 | mrbd | Predicted | SE | 95% CI |
|---|---|---|---|---|
| -0.14 | 0.00 | 6.10 | ||
| -0.10 | 0.00 | 6.12 | ||
| -0.06 | 0.00 | 6.13 | ||
| -0.02 | 0.00 | 6.14 | ||
| 0.02 | 0.00 | 6.15 | ||
| 0.06 | 0.00 | 6.16 | ||
| 0.10 | 0.00 | 6.17 | ||
| 0.14 | 0.00 | 6.19 | ||
| 0.18 | 0.00 | 6.20 | ||
| 0.22 | 0.00 | 6.21 |
Variable predicted: est_acc_div
Predictors modulated: inmigrante2
Predictors controlled: educacion_rec (1), libros_rec (1), apod_acc_div (6.6), civic (1), apdis_fa (-0.0072), mean_apdis_fa (-0.0036), etnia2 (0.35), escolaridad (8.6)
| escolaridad | mrbd | Predicted | SE | 95% CI |
|---|---|---|---|---|
| 6.11 | 0.00 | 6.24 | ||
| 6.74 | 0.00 | 6.22 | ||
| 7.36 | 0.00 | 6.20 | ||
| 7.98 | 0.00 | 6.17 | ||
| 8.60 | 0.00 | 6.15 | ||
| 9.23 | 0.00 | 6.13 | ||
| 9.85 | 0.00 | 6.10 | ||
| 10.47 | 0.00 | 6.08 | ||
| 11.10 | 0.00 | 6.06 | ||
| 11.72 | 0.00 | 6.04 |
Variable predicted: est_acc_div
Predictors modulated: escolaridad
Predictors controlled: educacion_rec (1), libros_rec (1), apod_acc_div (6.6), civic (1), apdis_fa (-0.0072), mean_apdis_fa (-0.0036), etnia2 (0.35), inmigrante2 (0.019)
We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict est_acc_div with educacion_rec (formula: est_acc_div ~ educacion_rec + libros_rec + apod_acc_div + civic + apdis_fa + mean_apdis_fa + etnia2 + inmigrante2 + escolaridad). The model included mrbd as random effect (formula: ~1 | mrbd). The model’s total explanatory power is weak (conditional R2 = 0.03) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to educacion_rec = Ed Basica, is at 5.95 (95% CI (5.20, 6.69), t(4783) = 15.71, p < .001). Within this model:
Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald t-distribution approximation., We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict est_acc_div with libros_rec (formula: est_acc_div ~ educacion_rec + libros_rec + apod_acc_div + civic + apdis_fa + mean_apdis_fa + etnia2 + inmigrante2 + escolaridad). The model included mrbd as random effect (formula: ~1 | mrbd). The model’s total explanatory power is weak (conditional R2 = 0.03) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to libros_rec = Menos de 25, is at 5.95 (95% CI (5.20, 6.69), t(4783) = 15.71, p < .001). Within this model:
Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald t-distribution approximation., We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict est_acc_div with apod_acc_div (formula: est_acc_div ~ educacion_rec + libros_rec + apod_acc_div + civic + apdis_fa + mean_apdis_fa + etnia2 + inmigrante2 + escolaridad). The model included mrbd as random effect (formula: ~1 | mrbd). The model’s total explanatory power is weak (conditional R2 = 0.03) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to apod_acc_div = 0, is at 5.95 (95% CI (5.20, 6.69), t(4783) = 15.71, p < .001). Within this model:
Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald t-distribution approximation., We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict est_acc_div with civic (formula: est_acc_div ~ educacion_rec + libros_rec + apod_acc_div + civic + apdis_fa + mean_apdis_fa + etnia2 + inmigrante2 + escolaridad). The model included mrbd as random effect (formula: ~1 | mrbd). The model’s total explanatory power is weak (conditional R2 = 0.03) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to civic = Bajo nivel D, is at 5.95 (95% CI (5.20, 6.69), t(4783) = 15.71, p < .001). Within this model:
Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald t-distribution approximation., We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict est_acc_div with apdis_fa (formula: est_acc_div ~ educacion_rec + libros_rec + apod_acc_div + civic + apdis_fa + mean_apdis_fa + etnia2 + inmigrante2 + escolaridad). The model included mrbd as random effect (formula: ~1 | mrbd). The model’s total explanatory power is weak (conditional R2 = 0.03) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to apdis_fa = 0, is at 5.95 (95% CI (5.20, 6.69), t(4783) = 15.71, p < .001). Within this model:
Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald t-distribution approximation., We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict est_acc_div with mean_apdis_fa (formula: est_acc_div ~ educacion_rec + libros_rec + apod_acc_div + civic + apdis_fa + mean_apdis_fa + etnia2 + inmigrante2 + escolaridad). The model included mrbd as random effect (formula: ~1 | mrbd). The model’s total explanatory power is weak (conditional R2 = 0.03) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to mean_apdis_fa = 0, is at 5.95 (95% CI (5.20, 6.69), t(4783) = 15.71, p < .001). Within this model:
Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald t-distribution approximation., We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict est_acc_div with etnia2 (formula: est_acc_div ~ educacion_rec + libros_rec + apod_acc_div + civic + apdis_fa + mean_apdis_fa + etnia2 + inmigrante2 + escolaridad). The model included mrbd as random effect (formula: ~1 | mrbd). The model’s total explanatory power is weak (conditional R2 = 0.03) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to etnia2 = 0, is at 5.95 (95% CI (5.20, 6.69), t(4783) = 15.71, p < .001). Within this model:
Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald t-distribution approximation., We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict est_acc_div with inmigrante2 (formula: est_acc_div ~ educacion_rec + libros_rec + apod_acc_div + civic + apdis_fa + mean_apdis_fa + etnia2 + inmigrante2 + escolaridad). The model included mrbd as random effect (formula: ~1 | mrbd). The model’s total explanatory power is weak (conditional R2 = 0.03) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to inmigrante2 = 0, is at 5.95 (95% CI (5.20, 6.69), t(4783) = 15.71, p < .001). Within this model:
Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald t-distribution approximation. and We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict est_acc_div with escolaridad (formula: est_acc_div ~ educacion_rec + libros_rec + apod_acc_div + civic + apdis_fa + mean_apdis_fa + etnia2 + inmigrante2 + escolaridad). The model included mrbd as random effect (formula: ~1 | mrbd). The model’s total explanatory power is weak (conditional R2 = 0.03) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to escolaridad = 0, is at 5.95 (95% CI (5.20, 6.69), t(4783) = 15.71, p < .001). Within this model:
Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald t-distribution approximation.
The model’s total explanatory power is weak (conditional R2 = 0.03) and the part related to the fixed effects alone (marginal R2) is of 0.02
---
title: "Regression model summary from `{easystats}`"
output:
flexdashboard::flex_dashboard:
theme:
version: 4
# bg: "#101010"
# fg: "#FDF7F7"
primary: "#0054AD"
base_font:
google: Prompt
code_font:
google: JetBrains Mono
params:
model: model
check_model_args: check_model_args
parameters_args: parameters_args
performance_args: performance_args
---
```{r setup, include=FALSE}
library(flexdashboard)
library(easystats)
# Since not all regression model are supported across all packages, make the
# dashboard chunks more fault-tolerant. E.g. a model might be supported in
# `{parameters}`, but not in `{report}`.
#
# For this reason, `error = TRUE`
knitr::opts_chunk$set(
error = TRUE,
out.width = "100%"
)
```
```{r}
# Get user-specified model data
model <- params$model
# Is it supported by `{easystats}`? Skip evaluation of the following chunks if not.
is_supported <- insight::is_model_supported(model)
if (!is_supported) {
unsupported_message <- sprintf(
"Unfortunately, objects of class '%s' are not yet supported in {easystats}.\n
For a list of supported models, see `insight::supported_models()`.",
class(model)[1]
)
}
```
Model fit
=====================================
Column {data-width=700}
-----------------------------------------------------------------------
### Assumption checks
```{r check-model, eval=is_supported, fig.height=10, fig.width=10}
check_model_args <- c(list(model), params$check_model_args)
do.call(performance::check_model, check_model_args)
```
```{r, eval=!is_supported}
cat(unsupported_message)
```
Column {data-width=300}
-----------------------------------------------------------------------
### Indices of model fit
```{r, eval=is_supported}
# `{performance}`
performance_args <- c(list(model), params$performance_args)
table_performance <- do.call(performance::performance, performance_args)
print_md(table_performance, layout = "vertical", caption = NULL)
```
```{r, eval=!is_supported}
cat(unsupported_message)
```
For interpretation of performance metrics, please refer to <a href="https://easystats.github.io/performance/reference/model_performance.html" target="_blank">this documentation</a>.
Parameter estimates
=====================================
Column {data-width=550}
-----------------------------------------------------------------------
### Plot
```{r dot-whisker, eval=is_supported}
# `{parameters}`
parameters_args <- c(list(model), params$parameters_args)
table_parameters <- do.call(parameters::parameters, parameters_args)
plot(table_parameters)
```
```{r, eval=!is_supported}
cat(unsupported_message)
```
Column {data-width=450}
-----------------------------------------------------------------------
### Tabular summary
```{r, eval=is_supported}
print_md(table_parameters, caption = NULL)
```
```{r, eval=!is_supported}
cat(unsupported_message)
```
To find out more about table summary options, please refer to <a href="https://easystats.github.io/parameters/reference/model_parameters.html" target="_blank">this documentation</a>.
Predicted Values
=====================================
Column {data-width=600}
-----------------------------------------------------------------------
### Plot
```{r expected-values, eval=is_supported, fig.height=10, fig.width=10}
# `{modelbased}`
int_terms <- find_interactions(model, component = "conditional", flatten = TRUE)
con_terms <- find_variables(model)$conditional
if (is.null(int_terms)) {
model_terms <- con_terms
} else {
model_terms <- clean_names(int_terms)
int_terms <- unique(unlist(strsplit(clean_names(int_terms), ":", fixed = TRUE)))
model_terms <- c(model_terms, setdiff(con_terms, int_terms))
}
text_modelbased <- lapply(unique(model_terms), function(i) {
grid <- get_datagrid(model, at = i, range = "grid", preserve_range = FALSE)
estimate_expectation(model, data = grid)
})
ggplot2::theme_set(theme_modern())
# all_plots <- lapply(text_modelbased, function(i) {
# out <- do.call(visualisation_recipe, c(list(i), modelbased_args))
# plot(out) + ggplot2::ggtitle("")
# })
all_plots <- lapply(text_modelbased, function(i) {
out <- visualisation_recipe(i, show_data = "none")
plot(out) + ggplot2::ggtitle("")
})
see::plots(all_plots, n_columns = round(sqrt(length(text_modelbased))))
```
```{r, eval=!is_supported}
cat(unsupported_message)
```
Column {data-width=400}
-----------------------------------------------------------------------
### Tabular summary
```{r, eval=is_supported, results="asis"}
for (i in text_modelbased) {
tmp <- print_md(i)
tmp <- gsub("Variable predicted", "\nVariable predicted", tmp)
tmp <- gsub("Predictors modulated", "\nPredictors modulated", tmp)
tmp <- gsub("Predictors controlled", "\nPredictors controlled", tmp)
print(tmp)
}
```
```{r, eval=!is_supported}
cat(unsupported_message)
```
Text reports
=====================================
Column {data-width=500}
-----------------------------------------------------------------------
### Textual summary
```{r, eval=is_supported, results='asis', collapse=TRUE}
# `{report}`
text_report <- report(model)
text_report_performance <- report_performance(model)
gsub("]", ")", gsub("[", "(", text_report, fixed = TRUE), fixed = TRUE)
cat("\n")
gsub("]", ")", gsub("[", "(", text_report_performance, fixed = TRUE), fixed = TRUE)
```
```{r, eval=!is_supported}
cat(unsupported_message)
```
Column {data-width=500}
-----------------------------------------------------------------------
### Model information
```{r, eval=is_supported}
model_info_data <- insight::model_info(model)
model_info_data <- datawizard::data_to_long(as.data.frame(model_info_data))
DT::datatable(model_info_data)
```
```{r, eval=!is_supported}
cat(unsupported_message)
```